Show HN: TERMy – A fast terminal assistant that does not use LLMs

Giovanni Blu, creator of the PJON network protocol, has released TERMy, a terminal assistant built on the NPC-Forge framework that translates natural language into shell commands without using LLMs, embeddings, or machine learning. Running in about 1,000 lines of Python, TERMy uses a lightweight NLU pipeline with exact, template, and probabilistic matching, and can operate on a Raspberry Pi Zero, responding in milliseconds. Blu developed TERMy over two months to avoid the rising costs of AI subscriptions, and its hardcoded permission gating for destructive commands is intended to be safer than LLM-based assistants.

I love research and development, you may have heard of me because of PJON Padded Jittering Operative Network . It is a network protocol I started developing in 2010, which was recently implemented in silicon by the ETH Zurich university thanks to the research of Pius Sieber. I am excited to share with you TERMy, a terminal assistant built on top of the NPC-Forge framework. Unlike everything else being built today, TERMy does not use embeddings, machine-learning or LLMs. It runs on the CPU even on a Raspberry Pi Zero both in the terminal or client-side in a browser tab and responds in milliseconds. It is a cynical but very knowledgeable Linux terminal assistant that translates your natural language into shell commands without relying on a single artificial neuron. I had a chance to focus for 2 months on my personal projects since early July, during the strange times of AI price hikes and the end of subsidized tokenmaxing. I was curious to see if I could develop from scratch a terminal assistant capable of handling simple natural language requests. I have a bad memory and got used to ask to copilot "activate the virtual environment" or similar trivial operations spending a non negligible sum every month. I started thinking, maybe I can do something to make my workflow more efficient? Do I really need trillions of parameters to accomplish those tasks? How it Works When you type a prompt, it goes through a lightweight NLU pipeline written in ~1000 lines of Python that implement the following steps: 1. Strip expletives, interjections, encouraging, discouraging and thanking words remove noise 2. Sentiment analysis 3. Exact Match very fast 4. Template Match slower 5. Probabilistic Match even slower Step 5 relies on: 1. IDF Inverse Document Frequency to identify rare words. 2. BOW Bag Of Words to accommodate word inversions. 3. IDF weighted Levenshtein to safely handle typos. Because permission gating is hardcoded into the dataset and enforced for all potentially destructive commands it's inherently safer than letting an unpredictable LLM run wild on your machine. - TERMy in operation: https://www.youtube.com/watch?v=qeIp0xePLBg https://www.youtube.com/watch?v=qeIp0xePLBg - Variance and typo tolerance: https://www.youtube.com/watch?v=tQvGDk6fkk0 https://www.youtube.com/watch?v=tQvGDk6fkk0 - Copilot integration: https://www.youtube.com/watch?v=Wzzouhq2a8A https://www.youtube.com/watch?v=Wzzouhq2a8A - Advanced features: https://www.youtube.com/watch?v=qeIp0xePLBg https://www.youtube.com/watch?v=qeIp0xePLBg - Source Code: https://github.com/gioblu/NPC-Forge https://github.com/gioblu/NPC-Forge Comments URL: https://news.ycombinator.com/item?id=49562219 https://news.ycombinator.com/item?id=49562219 Points: 1 Comments: 0